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Token Optimization Tools for LLMs & AI Coding Agents

Tools and techniques for spending fewer tokens — compressing context, caching prompts, routing to cheaper models and tracking usage — with a layered stack for AI coding agents.

62 topics•9 categories•Updated October 2026

Why Token Optimization Matters

3 entries

Tokens are the unit of cost, speed and context limits for large language models. Every file an agent reads, every log it prints and every word it replies with takes space in a finite context window and costs money, and long agent sessions can fill the window and trigger compaction that loses history. Optimization works at several points in the pipeline: avoid reading content at all (for example by querying an index instead of reading files), compress what enters the context, cache repeated prefixes, route simple work to cheaper models, shorten the model's own output, and measure usage so you can see what helps. An April 2026 Medium article by Abid Abdul Gafoor describes a five-layer stack for Claude Code that combines a codebase knowledge graph, output sandboxing, CLI output compression, an API-level compression proxy and a terse-output plugin, enforced with hooks, and reports session length rising from about 30 minutes to over 3 hours. Those are the author's own measurements on their workload, and the article itself cautions that the extra tool definitions can cost more than they save on tiny sessions. Treat any percentage on this page as a vendor or author claim and measure your own usage before and after.

01Source article (Medium)How I Cut Claude Code Token Usage by 90%+ With 5 Tools, Custom Hooks, and Enforcement, by Abid Abdul Gafoor (member-only story). 02Companion repository (claude-code-tips)The article author's configs, hooks and installer. The article warns it is a power-user setup with reduced permission prompts, so audit it before use. 03Managing costs effectively (Claude Code)Anthropic's documentation on tracking and reducing Claude Code token usage.

The Five-Layer Claude Code Stack

7 entries

The tools named in the source article, each at a different point in the pipeline.

04codebase-memory-mcpLocal MCP server that indexes a codebase into a knowledge graph so agents can query structure instead of reading files. Its own benchmark reports about 3,400 versus 412,000 tokens for five structural queries. 05context-modeMCP server and plugin that runs large tool outputs in a sandbox, indexes them and returns compact summaries that stay searchable. 06RTK (Rust Token Killer)Single Rust binary that filters and compresses command output before it reaches the model; the project reports up to 90% smaller output. 07HeadroomLibrary and proxy that compress tool outputs, logs, files and conversation history before they reach the LLM. The repository moved from the chopratejas account to headroomlabs-ai. 08CavemanClaude Code plugin that makes the agent reply more tersely and includes a command to compress CLAUDE.md files. 09CodeburnReads local session files from Claude Code, Codex and other tools and breaks down token cost by project, model and task. The repository moved from the AgentSeal account to getagentseal. 10ccusageCLI that analyzes local usage logs for Claude Code and similar tools; the repository moved from ryoppippi to the ccusage organization.

Claude Code & Anthropic Guidance

10 entries

Official documentation on context, hooks and caching.

11Claude Code: hooksRun commands before or after tool calls, the mechanism the article uses for enforcement. 12Claude Code: MCPConnecting MCP servers and managing which are enabled; each server's tool definitions use context. 13Claude Code: memory and CLAUDE.mdHow project instructions load into every session. 14Prompt caching (Anthropic)Reuse stable prompt prefixes to cut cost and latency. 15Token counting (Anthropic)Count tokens before sending a request. 16Batch processing (Anthropic)Run large asynchronous jobs through the batch API. 17Context windows (Anthropic)How context windows and limits work. 18Effective context engineering for AI agentsAnthropic engineering guidance on managing context for agents. 19Code execution with MCPAnthropic's write-up on cutting tool-definition and result tokens by having agents write code against tools. 20Writing tools for agentsDesigning tools that return token-efficient results.

Prompt & Context Compression

9 entries

Make the same information take fewer tokens.

21LLMLinguaMicrosoft research on compressing prompts with small language models. 22LLMLingua repositorySource code for LLMLingua and LongLLMLingua. 23LLMLingua paperCompressing prompts for accelerated inference of large language models. 24LongLLMLingua paperPrompt compression for long-context scenarios. 25RepomixPacks a repository into a single AI-friendly file with options to compress code. 26GitingestTurns a Git repository into a text digest for prompts; the repository moved from cyclotruc, and its last commit was in August 2025, so check its activity. 27TOONToken-Oriented Object Notation, a compact alternative to JSON for LLM input. 28Aider repository mapHow Aider summarizes a codebase for the model without sending every file. 29tree-sitterParser generator that many code-indexing tools use to extract structure.

Prompt & Semantic Caching

7 entries

Do not pay twice for the same prefix or question.

30OpenAI prompt cachingOpenAI documentation on caching repeated prompt prefixes. 31OpenAI Batch APIAsynchronous batch processing at a discount. 32Gemini context cachingGoogle documentation on caching context. 33GPTCacheSemantic cache for LLM responses. 34Redis LangCacheRedis semantic caching service for LLM applications. 35vLLM automatic prefix cachingReuse KV cache for shared prefixes when self-hosting. 36SGLangServing framework with prefix caching for structured LLM programs.

Routing, Gateways & Cost Tracking

9 entries

Choose cheaper models and see where tokens go.

37LiteLLMGateway and SDK for many model providers, with spend tracking and budgets. 38LiteLLM cost trackingTrack spend by key, team and model. 39Langfuse token and cost trackingToken and cost tracking inside LLM traces. 40HeliconeLLM observability and gateway. 41OpenRouterUnified API and routing across many models. 42RouteLLMFramework for routing queries between stronger and cheaper models; the last commit was in August 2024, so check its activity. 43AI GatewaysDirectory of AI and LLM gateways on this site. 44AI Model RoutersDirectory of model routers on this site. 45FinOps ToolsCloud and AI cost management on this site.

Token Counters & Tokenizers

4 entries

Know how many tokens you are sending.

46tiktokenOpenAI's fast tokenizer library. 47OpenAI TokenizerInteractive tokenizer; the site blocks automated checks. 48Hugging Face TokenizersFast tokenizer library used across many models. 49Gemini token countingCounting tokens for Gemini models.

Context Engineering & Agent Memory

5 entries

Keep only what the agent needs in context.

50Context engineering for agents (LangChain)LangChain's overview of write, select, compress and isolate strategies. 51LangMemLong-term memory tooling for agents. 52Mem0Memory layer for AI agents. 53LettaFramework for stateful agents with memory. 54Model Context ProtocolOpen protocol for connecting agents to tools and data.

Related Pages

8 entries

Neighbouring pages on this site.

55Top MCP ServersMCP servers directory. 56MCP Servers for AI DevelopersMCP servers for coding agents. 57AI Coding AgentsCoding agents directory. 58AI Coding ToolsTools for AI-assisted coding. 59LLMOpsOperating LLM applications. 60LLM Inference PlatformsPlatforms for serving LLMs. 61LLM Training and Inference OptimizationOptimization techniques. 62Agentic AIAgentic AI hub.

Frequently Asked Questions

What is token optimization?

Reducing the number of tokens an LLM application or coding agent reads and writes, without losing needed information, to lower cost, speed up responses and keep long sessions inside the context window.

Where in the pipeline can tokens be saved?

Before reading (query an index instead of reading files), as content enters context (compress command output or large tool results), at the API layer (compress or cache the prompt), in routing (use a cheaper model for simple tasks), and in the model's own output (ask for terse replies).

Do these tools always save tokens?

No. Each added tool or MCP server adds its own definitions to the context, which can cost more than it saves in very short sessions. The source article makes the same point. Measure usage with a tracker such as ccusage or Codeburn before and after.

How reliable are the quoted savings figures?

They are the authors' or projects' own measurements on specific workloads. Results depend on your code, commands and model, so test on your own tasks.

Are there security considerations?

Yes. Compression proxies, hooks and MCP servers can see prompts, code, logs and secrets, and installers may pull third-party code. Audit and pin dependencies, keep permission prompts on where appropriate, and avoid committing memory files that may contain secrets.

How current is this page?

Links and project activity were checked in October 2026. These tools are young and change quickly, and savings figures quoted on this page are the projects' or authors' own claims, measured on their own workloads. Sites that block automated checks are kept and may need a manual click.